3117results about "Other databases clustering/classification" patented technology

The invention discloses a knowledge base system construction method and device for multi-source heterogeneous data and a medium, and relates to the technical field of artificial intelligence and natural language processing. The method comprises the following steps: integrating a dynamic graph convolutional network and a hierarchical attention mechanism to construct a multi-modal document analysis engine; performing semantic structure analysis on the original heterogeneous document on the basis of a multi-modal document analysis engine to extract document structure features and content semantic features, and constructing an original document relationship model on the basis of the document structure features and the content semantic features; based on the original document relationship model, performing classification fusion on heterogeneous data in the original heterogeneous document to obtain a to-be-stored heterogeneous data corpus, and processing the to-be-stored heterogeneous data corpus by using a graph neural network to establish a cross-modal semantic association index; and based on the cross-modal semantic association index, performing classified storage on the to-be-stored heterogeneous data corpora by utilizing a preset heterogeneous database so as to complete knowledge base system construction of the multi-source heterogeneous data.

The invention relates to the technical field of AIGC content generation, discloses an AIGC content generation method and system based on multi-modal fusion, and aims to solve the problems of decentralization, low efficiency and insufficient originality of a traditional content generation tool. Multi-modal data such as texts, images, videos and audios are integrated, user intentions are analyzed in combination with intelligent retrieval and a domain knowledge base, automatic generation from multi-modal input to high-quality creative content is achieved, a cross-modal collaborative generation technology is adopted, semantic features are dynamically aligned, and logically coherent content is generated. The content emotional value is enhanced through an emotional analysis and dynamic optimization strategy, the homogenization bottleneck is broken through, meanwhile, an automatic quality evaluation and format adaptation mechanism is integrated, deep application of scenes such as text travel, advertisement, e-commerce and interactive network television service is supported, marketing copywriting, short videos and cross-platform distribution schemes can be efficiently generated, and the market competitiveness is improved. And the content production efficiency and the creativity transmission are obviously improved.

The invention provides an oil extraction equipment fault monitoring system and method, and belongs to the technical field of oil extraction equipment fault monitoring. The method comprises the following steps: acquiring operation data of oil extraction equipment, and performing feature extraction on the acquired operation data to obtain a target feature vector; fusing the obtained target feature vector with a historical fault case library and an oil extraction equipment physical constraint equation, and constructing a dynamically updated knowledge graph; based on the space-time causal adversarial network, analyzing the distribution offset of the target feature vector in the space-time dimension, detecting an abnormal event and outputting an abnormal type label; and according to the output abnormity type label, combining with a knowledge graph, tracing a propagation path of an abnormal event, and calculating a fault probability of a root cause component through a Bayesian network so as to carry out monitoring and early warning on the oil extraction equipment. According to the method, accurate fault detection and root cause positioning are realized through multi-modal data fusion and the dynamic causal knowledge graph, and the equipment shutdown risk and the operation and maintenance cost are remarkably reduced.

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The invention relates to the technical field of data analysis, can be applied to business scenes of medical health, financial science and technology, culture research and the like, and discloses a knowledge graph-based model analysis method, which comprises the following steps of: acquiring and processing target domain data, extracting and labeling keywords, constructing a knowledge graph comprising domain entities, a relational network and attribute characteristics, and analyzing the knowledge graph. Establishing a professional corpus and generating an analysis task, constructing a multi-dimensional analysis index system, inputting the analysis task to a to-be-analyzed model, obtaining a task output result, performing knowledge verification according to the knowledge graph, evaluating the knowledge verification result based on a multi-dimensional analysis index, and generating a final analysis result. According to the method, by fusing the knowledge graph and the multi-dimensional analysis indexes, the accuracy and comprehensiveness of evaluation are enhanced, the evaluation effect of the model in the aspects of professional knowledge understanding, reasoning ability and standard integrating degree is improved, the evaluation result is more reliable, and the application ability of the large model in the specific field is optimized.

The invention discloses a hyper-converged server multi-resource integration system and a scheduling method, belongs to the technical field of computer resource management, and aims at solving the problems that a traditional hyper-converged server is dispersed in resource scheduling, low in storage efficiency, difficult to identify abnormities and the like. Multi-source data is obtained by means of distributed acquisition nodes, a distributed time sequence database architecture is adopted for storage, a standard data set containing a multi-dimensional index is constructed, and efficient storage and rapid retrieval of the data are achieved. Positioning target data to construct a graph model, building a layered and partitioned distributed graph database, and capturing change events in real time to realize dynamic updating. And mining a resource causal relationship by using a graph neural network, and screening an effective causal chain. Meanwhile, according to a graph model, monitoring weights of nodes and connecting edges are calculated, differential monitoring is implemented, potential abnormal points are accurately identified, and a resource scheduling strategy is generated in combination with a causal relationship. Real-time feedback adjustment and database updating are performed during execution, multi-resource deep integration and intelligent scheduling are achieved, and the resource utilization rate and stability of the system are remarkably improved.

The invention relates to the technical field of database optimization, in particular to a slow SQL (Structured Query Language) statement optimization method and system, electronic equipment and a storage medium, the slow SQL statement optimization method comprises the following steps: preprocessing a collected target slow SQL statement to obtain multi-modal data of the target slow SQL statement; inputting the multi-modal data of the target slow SQL statement into the multi-modal large model, and performing feature extraction and fusion to generate comprehensive semantic representation; generating an optimization suggestion of the target slow SQL statement according to the comprehensive semantic representation; the optimization suggestions are verified, and then the optimization suggestions which are verified to be qualified are executed in a database. Through multi-dimensional data acquisition, large-model deep analysis and automatic strategy execution, full-life-cycle quality detection and optimization of the slow SQL statement are realized.

The invention provides a deep exploration AI reasoning method and system, and the method comprises the steps: generating a dynamic calculation graph based on a to-be-reasoned task, carrying out the risk assessment of the dynamic calculation graph, generating a risk prediction result, and carrying out the risk prediction of the to-be-reasoned task. The risk assessment at least comprises complexity assessment, memory occupancy assessment and data transmission quantity assessment; based on a risk prediction result, performing optimization processing on the dynamic calculation graph to generate an optimized calculation graph, the optimized calculation graph being used for constraining an inference calculation depth and an inference resource allocation range, the method converting natural language input into a semantic weighted dynamic calculation graph, and identifying a resource risk through node-level complexity assessment; performing self-adaptive pruning in combination with the permission level and reinforcement learning to generate an optimal calculation graph; elastic resource scheduling is implemented based on a multi-target model and real-time monitoring, so that the problem of low collaboration of reasoning depth and resource efficiency is solved.

The invention belongs to the technical field of multi-source heterogeneous data quality and analysis, and relates to a data management and intelligent analysis method oriented to power multi-source heterogeneous. According to the method, ontology model alignment, temporary ontology generation and multi-mode semantic embedding technologies are adopted, semantic unification of cross-data types is achieved, meanwhile, a spatio-temporal joint indexing mechanism is established, geographic coordinates and timestamps are bound and stored, efficient multi-dimensional query is supported, and data integration efficiency and semantic consistency are improved; the problem of poor adaptability of a fixed threshold value is solved by dynamically adjusting the operation data safety threshold value and evaluating the abnormal confidence coefficient, the detection precision and credibility of the power multi-source heterogeneous data anomaly are improved, the anomaly detection accuracy is improved, and the false alarm rate is reduced; by constructing an abnormity confirmation logic and combining equipment parameters, environment data and communication states, reasons such as equipment faults, environment interference and communication abnormity can be accurately positioned, root causes can be quickly positioned, and time can be shortened.

The invention belongs to the field of pollution prevention and control data processing, and particularly relates to a laboratory instrument data automatic acquisition and quality tracing system and a laboratory instrument data automatic acquisition and quality tracing method. A distributed search traceability mapping space is constructed through the storage mapping module to realize space-time-semantic composite indexing and classification detection of data, and a causal reasoning model of the quality judgment module is used for performing anomaly judgment; a reverse Hash traceability mechanism is innovatively designed, an index-traceability node graph and a segmented Hash heterogeneous node anchoring strategy are combined, Hash head, body and tail fragments are stored in three types of nodes of acquisition, storage and detection, and tamper-proof traceability of abnormal data and dynamic optimization of acquisition parameters are realized through cross-node aggregation verification; according to the invention, the problems of low data credibility and poor abnormal traceability efficiency in ecological environment monitoring are solved, and full-chain closed-loop management of data acquisition-analysis-traceability-acquisition configuration adjustment is supported.

The present application relates to the technical field of electronics, and provides a method for generating a commodity label on the basis of a large language model, and an electronic device. The method specifically comprises: acquiring commodity description information related to a target commodity; performing structured processing on the commodity description information to obtain an objective property feature of the target commodity; and using a fine-tuned large language model to process the objective property feature on the basis of a language prompt lexicon to obtain one or more commodity labels of the target commodity. According to the method, a commodity selling point label can be generated by means of the commodity description information acquired from multiple sources and multiple paths, the source of the commodity description information is expanded, commodity information of different sources can be identified and acquired by means of image and text details, etc., which can assist the model in better understanding the commodity, and generating a more reliable selling point label having a higher confidence level and greater traceability.

Providing generative artificial intelligence (AI)-enabled notebook interfaces for a security framework, including: receiving a request to generate a notebook interface for a security framework monitoring a cloud deployment; generating, in response to the request, the notebook interface, wherein the notebook interface comprises one or more notebook cells for interacting with the security framework, wherein the one or more notebook cells comprise a natural language input cell for querying a generative artificial intelligence (AI) model; and presenting the notebook interface.

The invention provides a customer group dynamic division method and system, and relates to the technical field of customer group management.The method comprises the steps that firstly, commodity related data are collected in real time through a multi-channel data collection module, commodity behavior analysis is conducted in combination with service requirements, features are extracted according to analysis results, and commodities are divided into multiple groups; and then, dynamically dividing customers by combining service requirements, generating initial customer group information, and optimizing and adjusting the customer group information according to the updated user portrait information. And finally, performing preference calculation according to the optimized customer group information, and generating a personalized push strategy. According to the method, through dynamic division and optimization of customer group information, unique requirements and behavior characteristics of each customer can be better met, and personalized services are provided.

The invention discloses a hollow slab girder bridge maintenance decision-oriented hierarchical knowledge graph construction method, which comprises the following steps of: constructing a hollow slab girder bridge disease classification and maintenance decision general knowledge graph, combing disease types and evaluation indexes of a hollow slab girder bridge through cross-standard feature alignment and rule fusion, and defining each disease and the index thereof in a variable manner so as to construct a hollow slab girder bridge disease classification and maintenance decision-oriented hierarchical knowledge graph. The weight of each disease index is determined based on a fuzzy-Bayesian hybrid reasoning algorithm, and a complete general knowledge graph structure is formed; extracting disease information based on a detection report of a single bridge, endowing each disease with a maintenance suggestion by using a general knowledge graph, and forming a maintenance decision knowledge graph special for the bridge; and dynamically updating the special knowledge graph, and adjusting disease information and maintenance suggestions according to a subsequent detection report of the bridge to ensure timeliness and accuracy of the graph. According to the method, the problems that knowledge is dispersed and systematization is difficult in traditional hollow slab girder bridge maintenance decision making are solved, and scientificity and efficiency of bridge maintenance management are improved.

The embodiment of the invention discloses a medical data privacy protection method and a computer device. The method comprises the following steps: firstly, acquiring medical industry laws and regulations and target medical institution data asset information; building a logic relation graph containing data classification and security level according to laws and regulations, and importing the laws and regulations information into a first graph database to obtain a laws and regulations graph; extracting a logical relationship in the data asset information, constructing a data logical relationship graph containing a library table column structure, and importing the data logical relationship graph into a second graph database to obtain an asset graph; business process information is collected, and a data flow relation is identified to perfect an asset map; analyzing the name and content of the data assets to obtain a classification and grading analysis result; on the basis of the result, constructing a heterogeneous graph neural network model by using a graph attention network, establishing a mapping relationship between the two maps, and updating the asset map; and finally, carrying out privacy protection on the data assets according to the updated asset atlas. According to the method, the accuracy and efficiency of medical data classification and grading can be improved, and the privacy protection capability is enhanced.

The invention discloses a robot interactive question-answering method and system based on a large service model, and belongs to the technical field of intelligent robots, and the method comprises the steps: carrying out the feature extraction and fusion of the original input of a user, obtaining a multi-modal vector, building a recognition model, outputting an emotion recognition tag, an intention recognition tag, a user state vector and a session history, fusing the user state vector with a domain knowledge graph, outputting a structure reasoning vector and a joint condition vector, and splicing a knowledge abstract vector with a condition fusion vector to obtain the user state vector as the input of a text generator; and outputting a natural language answer and a user state vector, mapping the natural language answer into a multi-modal behavior action, and finally outputting a voice answer signal and the multi-modal behavior action. According to the method, the answer strategy of the robot is dynamically adjusted according to the emotional state of the user by combining context awareness and knowledge reasoning technologies, so that intelligent interaction of emotional resonance and professional depth coexistence is realized.

The invention relates to the field of enterprise industry chain evaluation, and discloses an artificial intelligence-based enterprise industry chain multi-dimensional evaluation system and method, and the system comprises a data collection module which is used for obtaining industry chain data from a structured database, an unstructured text and a third-party platform; the data fusion module is used for constructing a dynamically updated industrial chain knowledge graph through a natural language processing technology; the multi-dimensional evaluation module comprises a financial health evaluation sub-module, a supply chain toughness evaluation sub-module, an innovation power evaluation sub-module and a carbon emission evaluation sub-module, and each sub-module generates quantitative scores by adopting an LSTM neural network, a PageRank algorithm and a space-time diagram convolutional network model and weights the quantitative scores to obtain comprehensive scores; the risk early warning module is used for simulating a triple conduction effect of an external event on an industrial chain based on a graph neural network and generating a risk index and a coping strategy; and the visual interaction interface dynamically displays the evaluation result. According to the method, the problems of data splitting and poor dynamic adaptability in a traditional evaluation method are solved, and accurate evaluation of the industrial chain is realized.

The technology described herein relates to systems, methods, and computer storage media, among other things, for providing an enhanced conceptualized search based on enriched categorizations of item listings. For example, the enhanced conceptualized search can include generating a search vector, using one or more generative artificial intelligence (AI) models, based on a received search query. Some embodiments of generating the search vector may include applying one or more generative AI models to one or more sets of prior user interactions. Based on the search vector, a particular item listing vector can be determined. The item listing vector can be generated by applying one or more generative AI models to each of an image of the item and a textual description of an item listing, and the item listing vector can be generated based on enhanced categories determined (e.g., using one or more generative AI models) for the item listing.

The invention discloses a public information publishing platform and method, and relates to the technical field of information public facilities, and the method comprises the following steps: obtaining to-be-evaluated information and publisher data, and analyzing to generate a credibility score PA of an information source; performing sentiment analysis and keyword inspection on the to-be-evaluated information, identifying sentiment deviation and semantic contradiction in the to-be-evaluated information, and analyzing a credibility score PB of the generated content; and acquiring an external authoritative data source, comparing the external authoritative data source with a time consistency check algorithm by using a text similarity algorithm, judging the consistency of the to-be-evaluated information and the external authoritative data source, and outputting a consistency reliability index Pc. According to the method, the problems of low false information identification efficiency and poor accuracy in the prior art are effectively solved by combining multi-dimensional data processing such as information source credibility evaluation, content analysis, external authoritative data source comparison, propagation path analysis and the like.

A method and an apparatus for generating a motion of a virtual character, and a method and an apparatus for constructing a motion library of a virtual character are provided, and belong to the field of computer technologies. The method for generating a motion of a virtual character includes: obtaining audio and text of a virtual character, the text indicating semantic information of the audio (201); determining a semantic tag of the text based on the text (202); retrieving a motion category matching the semantic tag and motion data belonging to the motion category from a preset motion library (203); and generating a motion sequence of the virtual character based on the motion data (204).

The invention discloses a material weighing and conveying control method and system, and relates to the technical field of self-adaptive control systems. The method comprises the following steps: setting a visual positioning identifier in an unloading area, and configuring an identification code for a material; establishing an information management database, associating the identification codes of the materials with material attributes, and generating a material association attribute table; collecting an identification code of the material, and calling a material association attribute table based on the identification code; and based on the information management database, obtaining a production line state table of each production line and a warehouse state table of the current warehouse, generating a candidate transportation list, determining a comprehensive priority score of each candidate transportation target point, and selecting the candidate transportation target point with the highest score as a final transportation target point. Compared with the prior art in which fixed rules are used for scheduling, the material transportation targets can be dynamically distributed according to real-time production requirements, manual intervention is avoided, the logistics efficiency is improved, high-requirement materials are preferentially distributed, and production interruption caused by material shortage is prevented.

The invention relates to the technical field of image recognition and storage, and discloses a driving video recording method and system based on four-way monitoring. The method comprises the following steps: synchronously acquiring and processing the surrounding environment of a vehicle and the head posture of a driver through a video stream to obtain driving attention reference data; compressing and storing the sight blind area data according to the head posture features to obtain hierarchical video data; performing enhanced mapping on the graded video data to obtain an attention compensation image; performing threat identification and sorting on the compensation image to obtain threat level data; establishing a threat level index structure; and performing hierarchical storage on the data according to the attention coverage degree to obtain a storage data set. According to the method and the device, the technical problem that key scene data may be lost due to lack of consideration of driver attention distribution in a multi-channel video real-time recording process is solved.

The invention relates to the technical field of industrial waste heat recovery optimization, in particular to an industrial circulating cooling water waste heat multi-source coupling system based on data driving, and the system comprises a waste heat collection unit, a data processing unit and an intelligent coupling regulation and control unit. After the data processing unit preprocesses data, high, medium and low temperature layers are divided according to temperature, a clustering algorithm is combined to subdivide a calorific value sub-layer, a dynamic characteristic file containing a parameter change curve, fluctuation amplitude and duration is established, the intelligent coupling regulation and control unit compares the file with pre-stored user heat demand characteristics, the matching degree is calculated preferentially according to the temperature contact ratio, and the matching degree is calculated preferentially. A single or multi-source cooperative coupling path is generated, target thermal parameters are synthesized through thermal capacity weighting in a multi-source mode, the supply and demand matching precision and the utilization efficiency of waste heat are improved, diversified energy consumption requirements of industrial scenes are met, and energy conservation and emission reduction are facilitated.

The invention discloses a large-span continuous rigid frame construction line shape intelligent monitoring method, and relates to the technical field of large-span continuous rigid frame construction, and the method comprises the following steps: S1, data collection; s2, data processing; s3, performing multi-stage intelligent early warning; s4, construction regulation and control; and S5, outputting a result. According to the method, multi-source equipment is used for synchronously collecting data, the real-time performance and integrity of the data are guaranteed, deep learning is used for eliminating anomalies, a neural network is used for constructing an environment influence model and correcting the data, the influence of environment factors on linear data is corrected, digital twinning is combined with a finite element and a neural network to predict a deformation trend, and the prediction precision is improved. A linear deviation trend is pre-judged in advance, a construction scheme is optimized, multi-level intelligent early warning dynamically adjusts a threshold value according to multiple factors, early warning information is accurately issued, risk response timeliness is enhanced, decision suggestions can be automatically generated through construction regulation and control, an evaluation effect is fed back, and the construction process is optimized.

The invention discloses a knowledge model data management system based on artificial intelligence, and relates to the field of data management, and the system comprises the following components: a data collection and storage module, an intelligent prediction and analysis module, an abnormal comprehensive processing module, a knowledge graph and traceability module and a man-machine interaction management module. According to the invention, the intelligent prediction analysis module is combined with a time sequence prediction model and a causal reasoning algorithm, abnormal fluctuation and causal association thereof in data can be accurately judged, the accuracy of anomaly detection is effectively improved, and meanwhile, the knowledge graph and traceability module uses the constructed knowledge graph and reinforcement learning algorithm to improve the accuracy of anomaly detection. According to the method, the relation chain can be quickly traced from the abnormal data, the problem source can be positioned, the abnormal traceability efficiency is remarkably improved, and the functions act together, so that the system can more quickly and accurately discover and process problems when facing a complex data environment, and the stability and reliability of data management are guaranteed.

The invention discloses a die stamping process optimization method and system, and relates to the related field of mechanical manufacturing, and the method comprises the steps: obtaining a data set of a workpiece, and extracting the features of the workpiece; matching simulation of the workpiece and the matching part is carried out, and a restrictive space of the workpiece is established; inputting the workpiece features and the product requirements of the workpiece into a structural feature analysis model to execute structural analysis, and establishing a structural sensitive identifier; performing mold optimization fitting, and establishing a mold reinforcing rib scheme set; performing stamping key parameter optimization based on the scheme set, and establishing a control parameter optimization set; and performing evaluation analysis on a mapping result through a multi-dimensional fitness evaluation function to generate a process optimization scheme. The technical problem that in an existing die stamping process optimization method, die design and process parameter adjustment are carried out separately, integrity and collaboration are lacked, and the optimization effect is limited is solved, and the technical effect that die design and process optimization are closely combined, and comprehensive optimization from die design to process parameters is achieved is achieved.

The invention belongs to the technical field of data processing. According to the multi-source heterogeneous data multi-mode mixed retrieval method and system based on large model reasoning, in the reasoning process of each step, a large language model determines sub-query questions of different retrieval modes and corresponding sub-query answers according to an initial query question; according to the sub-query question and the sub-query answer corresponding to the query mode of each inference process, adaptively determining an optimal retrieval path by taking the maximum reward function as a target, and determining a final answer corresponding to the initial query question according to the optimal retrieval path; wherein the optimal retrieval path comprises an optimal answer of each retrieval mode and a retrieval sequence of each retrieval mode, and the retrieval modes comprise database retrieval, semantic retrieval and atlas retrieval; according to the method, multi-hop reasoning of complex query is supported while the retrieval efficiency is ensured, dynamic fusion of multiple types of retrieval modes is realized, and the retrieval efficiency and the retrieval precision are greatly improved.

The invention relates to an intelligent marketing content generation and optimization system, and belongs to the technical field of artificial intelligence and digital marketing. According to the system, through cooperative operation of the data acquisition module, the intelligent text generation module, the multi-modal synthesis module, the risk management and control module and the dynamic optimization module, whole-process closed-loop management of marketing content is realized. The data acquisition module captures unstructured data from a multi-source platform based on a user authorization protocol, semantic cleaning and structured processing are performed through a large model interface, and the problems of data dispersion and fragmentation are solved. The intelligent text generation module receives user parameters through an interactive interface, matches a preset prompt word bank, calls a large model interface to generate multi-version copywriting in batches, and supports concurrent processing of single-choice or multi-choice product lists. The multi-modal synthesis module extracts keywords according to copywriting semantics, matches pre-annotated visual materials and generates multilingual speech paraphrases, so as to ensure the consistency of graphic and text information. The risk management and control module intercepts sensitive content in real time based on an industry rule base, and guarantees output compliance. And the dynamic optimization module integrates the user behavior data to generate a delivery strategy, updates a prompt word bank and a generation model through a closed-loop feedback mechanism, and realizes dynamic adaptation of contents and market demands. Compared with a traditional method, the system has the advantages that the content generation efficiency, the cross-modal matching precision and the compliance control level are remarkably improved, and the system is suitable for the global digital marketing requirements of multiple industry scenes such as e-commerce, industrial equipment and cross-border trade.

The present disclosure relates to systems, non-transitory computer-readable media, and methods for generating a content stack utilizing one or more machine-learning models. In some implementations, the disclosed systems generate and provide, to a user account, a content stack that includes content items corresponding to a topic prompt for the user account. For instance, in some implementations, the disclosed systems utilize content-based signals and account-based signals to generate an account-specific stack formulation graph that represents a plurality of content items and relationships of the content items with each other and with the user account. Additionally, in some implementations, the disclosed systems analyze the account-specific stack formulation graph to generate a content stack from the plurality of content items, the content stack comprising a set of content items corresponding to the topic prompt.